Identity-Conditioned Score Fusion for Open-Set Person Re-Identification

Abstract:Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query quality, model strength also varies across identities. We introduce identity-conditioned score fusion, a framework that tailors weights to each gallery identity without training. By contrasting intra-identity consistency against cross-identity impostors, it extracts identity-specific profiles that couple with query-conditioned adaptation via a parameter-free rule. This widens the separation between true and false matches while preserving score calibration. Evaluations on three clothes-changing person re-identification benchmarks show that our method consistently outperforms statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate and demonstrating the value of identity-conditioned fusion in open-set person re-identification.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2610.07366 [cs.CV]

(or arXiv:2610.07366v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.07366 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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